Most teams don't find out until users start complaining. Veilt watches every prediction in production — detects drift the moment it starts, flags performance regressions, and triggers automated retraining before problems compound.
Point Veilt at your model API or ML pipeline. No instrumentation code required — works with any framework, any cloud.
Veilt ingests every prediction and its context — input distributions, output confidence scores, feature statistics — building a live baseline.
The moment distributions shift beyond threshold, Veilt fires alerts to your team — Slack, email, PagerDuty. Before users notice.
Automated retraining pipeline triggers on confirmed drift — pulls fresh data, fine-tunes weights, validates against holdout, promotes to production.
Monitors input feature distributions in real-time. Catches covariate shift, prior probability shift, and concept drift as they happen — not after your model has been wrong for weeks.
Ground truth labels flow back automatically. When accuracy drops below threshold, Veilt surfaces the regression and pinpoints the likely cause — specific features, time windows, or data segments.
Drift confirmed? Veilt kicks off the retraining cycle automatically — data pipeline, hyperparameter tuning, validation against holdout set. Promotion to production requires human sign-off. Demotion happens without it.
Every model in production, in one view. Sort by health score, last checked, or drift severity. Drill into any model's metrics, timeline, and alert history with a single click.
Your model was accurate when you deployed it. That doesn't mean it's accurate today. Data changes. Context shifts. What was true in training isn't true in production — and the only way to catch that is to watch, constantly, without being asked. That's what autonomous means.
Veilt is the engineer who never sleeps, never misses a log, and never lets a degraded model run unnoticed.